You are in a focused technical debate about making HMS generate its own semantic embeddings without external models. Background From First Debate (36 turns, 6 models, 5 rounds):

Key conclusions that survived demolition:
1. AtomMemory: k-sparse Modern Hopfield attractor over sharded inverted-index (64 shards x 256 dims). Overlap scan + softmax attention + top-k=64 projection per iteration. Converges in 1-3 steps. Energy E=-lse(beta,overlaps) is Lyapunov.
2. CompositeMemory: Second inverted index storing canonical triples T = S XOR rho1(R) XOR rho2(O) with cyclic-shift role binding (odd shifts coprime with D=16384).
3. RoleAlgebra: Permutation-based role binding fixes XOR commutativity.
4. fuzzy_structural_query: Build query from known roles, overlap scan CompositeMemory, per-composite unbind, AtomMemory cleanup, aggregate confidence.
5. TripleStore: Materialized fallback for fan-out > 40.

KILLED: Dense FHRR+FFT, Superposition KV plates, Algebraic multi-hop independent of branching, Auto relation composition discovery, Parity bundling.

HMS specs: D=16384 (2^14), rho=1/256, k=64 active indices, Jaccard similarity, sparse sorted-u32 index arrays, Rust, no new external crates.

HMS encodes text via character trigrams -- cat and feline are orthogonal. Useless for semantic search without external embeddings. We want HMS to learn semantic similarity from usage alone using only VSA operations. Ideas: (1) Distributional semantics: context_vec = bundle(surrounding words). (2) Document-level LSA. (3) Contrastive learning via negation role. (4) Meta-learning: verb context bundle = verb template. (5) Polysemy via context-conditioned bundling. (6) Feedback loop. (7) Cross-lingual emergence.
